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setErrorModel

Set the error model type to be used for the observation model(s). Call getObservationInformation to get a list of the available observation names within the current project.

Usage

R
setErrorModel(...)

Arguments

...

A list of comma-separated pairs {observationModel = (character)errorModelType}.

Details

Available error model types are :

"constant"obs = pred + a*err
"proportional"obs = pred + (b*pred)*err
"combined1"obs = pred + (b*pred^c + a)*err
"combined2"obs = pred + sqrt(a^2 + (b^2)*pred^(2c))*err


where a, b, and c are parameters, obs is the observed data, pred is the prediction from the structural model, and err is normally distributed with mean 0 and variance 1. Error model parameters will be initialized to 1 by default. Call setPopulationParameterInformation to modify their initial value.
The value of the exponent parameter c is fixed by default when using the "combined1" and "combined2" models. Use setPopulationParameterInformation to enable its estimation.

See also

getContinuousObservationModel get the current observation model for the current project
getObservationInformation to get the continuous observations present in the current project
setPopulationParameterInformation to update error model parameters to be estimated

Set components of the continuous observation model(s):
setObservationDistribution
setObservationLimits

Examples

R
initializeLixoftConnectors("monolix")
project <- file.path(getDemoPath(), "1.creating_and_using_models",  "1.1.libraries_of_models", "warfarinPKPD_project.mlxtran")
loadProject(project)

# get observation model names available in the current project, how they are mapped to the data,
# and how they are mapped to the predictions
getObservationInformation()$name
#> [1] "y1" "y2"
getObservationInformation()$mapping
#>  y1  y2 
#> "1" "2" 
getContinuousObservationModel()$prediction
#>   y1   y2 
#> "Cc"  "R" 

# update the error model
setErrorModel(y1 = "proportional", y2 = "combined1")

getContinuousObservationModel()$errorModel
#>             y1             y2 
#> "proportional"    "combined1" 
getContinuousObservationModel()$formula
#>                           y1                           y2 
#>      "y1 = Cc + b1*Cc * e\n" "y2 = R + (a2 + b2*R) * e\n" 

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